Hi, I am a doctoral researcher at ETH Zürich and MPI-IS in Tübingen, advised by Celestine Mendler-Dünner (MPI) and Andreas Krause (ETH).
My research focuses on test-time training and preference alignment, often in the context of large language models. I am particularly interested in developing a theoretical understanding of these techniques and in characterizing their potential to improve model performance. Ultimately, I aim to leverage these insights to design robust and adaptive learning algorithms.

Projects & Publications
Specialization after Generalization: Towards Understanding Test-Time Training in Foundation Models
ETH Zürich, September 2025Oral Presentation at NeurIPS 2025 CCFM WorkshopSpecialization after generalization in foundation models through test-time training, with empirical and theoretical analysis.
Understanding Gradient Flow Dynamics for Matrix Factorization Problems
Harvard & ETH Zürich, February 2024 – September 2024Awarded an ETH MedalHigh-dimensional characterization of gradient flow dynamics for the matrix factorization problem, based on advanced tools from random matrix theory.